97 citations · 489 across the 41 of their papers we have counts for
32 papers · 1 filter
TRIUNE-Net: Harmonizing Scale, Shape, and Efficiency in Pancreatic Tumor Segmentation
Amir Hossein Saleknia, Alireza Kheyrkhah, Sanaz Karimijafarbigloo +5
Pancreatic tumor segmentation in 3D CT volumes is challenged by extreme scale variability across both the pancreas and tumor, and highly irregular tumor morphology. While recent ad…
Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation
Sanaz Karimijafarbigloo, Armin Khosravi, Alireza Kheyrkhah +3
Multi-rater medical image segmentation captures the inherent ambiguity of clinical interpretation, where diagnostic boundaries vary across experts and imaging devices. Existing app…
CENet: Context Enhancement Network for Medical Image Segmentation
Afshin Bozorgpour, Sina Ghorbani Kolahi, Reza Azad +2
Medical image segmentation, particularly in multi-domain scenarios, requires precise preservation of anatomical structures across diverse representations. While deep learning has a…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
SLA-INR: Single-Layer Learnable Activation for Implicit Neural Representation
Moein Heidari, Reza Rezaeian, Reza Azad +3
Implicit Neural Representation (INR), leveraging a neural network to transform coordinate input into corresponding attributes, has recently driven significant advances in several v…
Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook
Reza Azad, Moein Heidary, Kadir Yilmaz +5
Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predomi…
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Amirhossein Kazerouni, Sanaz Karimijafarbigloo, Reza Azad +3
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training. In response to this challenge, we introduce Fu…
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko +2
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-la…
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
Afshin Bozorgpour, Bobby Azad, Reza Azad +3
Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or…
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
Amirhossein Kazerouni, Reza Azad, Alireza Hosseini +2
Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. How…
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision
Bobby Azad, Reza Azad, Sania Eskandari +4
Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning proble…
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
Reza Azad, Amirhossein Kazerouni, Alaa Sulaiman +4
Medical image segmentation is a critical task that plays a vital role in diagnosis, treatment planning, and disease monitoring. Accurate segmentation of anatomical structures and a…
Self-supervised Semantic Segmentation: Consistency over Transformation
Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni +3
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical…
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
Reza Azad, Leon Niggemeier, Michael Huttemann +5
Medical image segmentation has seen significant improvements with transformer models, which excel in grasping far-reaching contexts and global contextual information. However, the…
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
Reza Azad, Amirhossein Kazerouni, Babak Azad +4
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it ha…
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach
Sanaz Karimijafarbigloo, Reza Azad, Dorit Merhof
Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, exi…
Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach
Reza Azad, Yiwei Jia, Ehsan Khodapanah Aghdam +2
While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-ra…
Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review
Reza Azad, Amirhossein Kazerouni, Moein Heidari +6
The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transfo…
DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
Reza Azad, René Arimond, Ehsan Khodapanah Aghdam +2
Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the…
TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model
Reza Azad, Mohammad T. AL-Antary, Moein Heidari +1
In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been the prevailing technique in the medical image processing era. Specifically, the seminal U…
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Moein Heidari, Amirhossein Kazerouni, Milad Soltany +4
Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and…
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
Reza Azad, Nika Khosravi, Dorit Merhof
Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells. In theory…
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Reza Azad, Moein Heidari, Julien Cohen-Adad +2
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral frac…
Semi-supervised few-shot learning for medical image segmentation
Abdur R Feyjie, Reza Azad, Marco Pedersoli +3
Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models requ…